A brief glossary of annoying expressions in science communication

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Photo source: pixabay.com

In response to my post “Where Have All The Bad Scientific Talks Gone?”, I’ve received a variety of feedback from readers who express exasperation about the way science and innovation are often discussed.

There are concerns that, in general, these areas are becoming overcrowded with growing levels of clichés and even nonsensical terms. Expressions that possibly echo delusions of grandeur, demonstrate the disconnection between what a person “thinks” and what is actually known, or simply conceal a disregard for evidence.

In these conversations, multiple instances of irritating expressions have been highlighted, which can be found in a wide range of media: from scientific publications and all kinds of meetings to official business communication and journalism. A selected glossary of such commonly used expressions follows.

“I am not an expert, but…”

Common usage:  “I am not an expert, but I think you should…”, or variations of the sentence following “but”. This is an archetypal indicator of an incoming cloud of conceit, especially in meetings with a multi-disciplinary audience. However, it can be heard in different contexts and is not necessarily used by researchers only.

Likely origin: Some people’s need for constant validation.

Possible actual meanings: I am going to say something to justify my presence in this meeting, or alternatively: my opinion matters more than years of research or scientific consensus. Also used to avoid a proper argument or hide behind a screen of false modesty.

“At the end of the day”

Common usage:  “You know, at the end of the day…”, “I agree, but at the end of the day…”. This expression is also a major hit in tech transfer and entrepreneurial circles.

Likely origin: Anglo-American sport commentating and politics.

Possible actual meanings: I will ignore what everybody else has just said, and will try to politely impose my wishful thinking. Conversely, it may indicate that the user has just run out of ideas.

“Shedding light”

Common usage:  This is not only a classic expression in science journalism, e.g., “Scientists shed light on…”, but also in research articles, e.g., “This study sheds light on…” Alternative use: “throwing light”.

Likely origin:  The clairvoyant community.

Possible actual meanings: There is nothing truly exciting here, but it is a well-crafted and nicely reported incremental advance.

“Leverage”

Common usage:   “In this research we leverage…”, “this project leverages resources…”

Likely origin: Financial sector nomenclature. Its extensive use may be a symptom of banking sector envy.

Possible actual meanings:  We are doing a bunch of things, and together they will eventually lead up to other bigger, hopefully fundable things.

“Visionary”

Common usage:  Different versions of he/she “is a visionary in the field of…”

Likely origin: Business-oriented literature that is easily recognizable at airport bookstores.

Possible actual meanings:  When this characterization is clearly unmerited, it may suggest that either the term user or its recipient have limited practical skills. When used as a self-description, it is surely a sign of illusory male overconfidence.

We may interpret the overuse (or misuse) of these and other expressions in different ways.  Some of us may argue that this is just a reflection of the homogenization of language in science communication in general. Others may point out that some of these expressions highlight the pressure to show, particularly to funders and politicians, that research and education can become highly efficient engines of profit maximization.

Or perhaps there is something more troubling. Are researchers and the public increasingly establishing fuzzier boundaries between opinions and facts? Or maybe it is another prominent feature of societies that are getting used to confusing overvalued expectations with actual effects, desires with needs, and reality with delusion.

This post was originally published in the United Academics Magazine.

What makes a book your favorite book?

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Photo source: pixabay.com

The philosopher Francis Bacon was of the opinion that “some books should be tasted, some devoured, but only a few should be chewed and digested thoroughly”. Poet W. H. Auden put it another way: “Some books are undeservedly forgotten; none are undeservedly remembered”.

Among President Obama’s top-5 favorite books are Moby Dick and The Bible. One of Bill Gates’s favorite books is Business Adventures, while Elon Musk prefers Lord of the Rings and The Hitchhiker’s Guide to the Galaxy. Gabriel García Márquez put books by Joyce and Kafka in his top list. Susan Sontag’s selection included titles by Tolstoy and Goethe. David Foster Wallace went for those by C.S. Lewis and Stephen King. Among Alan Turing’s library-borrowed books there were not only science titles, but also several by Lewis Carroll. David Bowie’s list included a wide range of novels, poetry and politics.

What made those books their favorite books?

There are books people think that should be their favorite books. These are the books “everybody” talks about, the “important” ones, and those that you are supposed to admire. The books that other people expect you to publicly advertise: Because of your social status, religious inclinations or ideological loyalties.  Some of these books are undeservedly remembered.

And then there are your truly favorite books. The ones that you may even love. The books that you fully appreciated on first reading, the ones that have always been with you, and those that you learned to value on a second (or more) readings.  These are the books that do not have to be in other people’s favorite picks. The books that you “digested thoroughly”, the ones that sometimes are undeservedly forgotten.

What makes a book fall into the latter category?

Clearly, there are not generalizable rules. Despite commonalities among individuals, this is a very personal experience. An honest selection may require whispering to yourself. Listing your top choices may seem like a secret ritual, a childhood’s hidden treasure.

And yet, it’s often possible to distill in a few words your “reasons” for caring about a particular book, to approximate your feelings in a few sentences. An essence that may sound familiar to others, almost universal.

Islands (Les Îles) is a book of essays by French writer Jean Grenier. The book combines childhood and adult memories, including those on Grenier’s cat Maoulou. Grenier was a teacher of Nobel laureate Albert Camus, and Islands became one of the top favorite, most influential books in Camus’ life.

Why this one in particular?

To Camus, the reason was simple and yet potent: The book triggered in him a colossal desire to write. In Camus’ words: “Something, someone, was stirring in me, obscurely, and wanted to speak”.

In this example, like in many others, it seems that your greatest reads are those that reveal to you a new way to be, or to do.  A window into discovery. Not an escape or diversion from a particular reality, but a decoded invitation to peer and eventually enter other realities: Augmented, thrilling or just different.

However, it does not have to be a revelation, or even an illumination.

Your favorite books may also condense an evocation: of birthplace, youth, hope, loss, resistance or joy. Or they could be reminders of your sense of a better life, of a possible life: past or to be accomplished.  Moments of reconciliation with life.

Perhaps you found your favorite book a long time ago, or maybe you are still looking for it. In any case, it’s always a good time to answer to yourself: What makes that book my unforgettable book?

Published in Medium.

The perks of data sharing

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“Pattern 7” by F. Azuaje

Data sharing is no longer a question of ‘why’, but rather of ‘when’ and ‘how’.

The access to biomedical research data is both a critical requirement and concern in the road to generating benefits to patients and society at large. In the scientific community there seems to be two “extreme” opinion sectors: those who firmly oppose steps for making data more accessible to all, and those who seek ways to free data of any restrictions for further uses.

The first group argues that the researchers responsible for acquiring the data should be the exclusive “owners” of that data. In this group you may find scientists who see other potential data users as “research parasites”. On the other hand, in the second group you have researchers who argue that data should be made available to the community without delays and favor full data openness.

Whether we feel closer to one group or the other, we cannot overlook a fact: It is time to talk about data sharing in a more dispassionate manner. Such a conversation will need to address two central questions: When and how should data be shared?

J. Wilbanks and S.H. Friend at Sage Bionetworks (Seattle, USA) have recently made a significant contribution to this conversation by reporting their motivation and experience in health data sharing. This follows Sage Bionetworks’ decision to share data obtained from thousands of participants of the mPower project, a smartphone-enabled study in Parkinson’s disease, even before the publication of their own analyses.

Data sharing: When?

According to Wilbanks and Friend, data sharing is especially needed in research areas where the problem of transforming raw data into interpretable findings has no generalized solutions. In their area, mobile health research, there is still a need to develop computational methods for making sense of these data. They argue that, by rapidly sharing such data, researchers will be enabled to come up with new tools to accelerate discoveries and applications, which in the long-term may result in benefits to patients.

Data sharing: How?

Scientists not only have a duty to maximize the potential value of their data, but also to enhance the conditions for their ethical use. To address these obligations, Sage Bionetworks’ approach does not solely rely on researchers or ethical committees to decide on who can re-utilize data. Instead, they directly allow the study participants to decide on whether or not other “qualified researchers” can access their coded data. In their project, more than 75% of the study participants chose to share their data widely. Participants can make this decision, or even modify it at any time, by using the study’s smartphone app.

Once researchers are given data access, additional restrictions are put in place, such as those concerning the commercial use or re-identification of the data. Additionally, there is the question of who can be recognized as a qualified researcher. To deal with this issue, Sage Bionetworks ask data requestors to complete various steps, including the validation of their identity and agreement to a data sharing contract.

Data sharing: Patients first.

Sage Bionetworks’ data sharing approach offers insights that go beyond the question of whether or not data should be shared. It reframes the discussion as a question of when (and how) to share data, according to the specific context and needs of a study. Although several legal and ethical concerns still remain, this approach at least aims to balance crucial requirements: the participants’ privacy and their motivation to support research, while promoting the transparent and ethical use of the data.

We should enhance the decision-making power of study participants to facilitate responsible and meaningful data applications. Decisions on when and how to share data should not be driven by the self-interest of scientists. The rationale should be grounded in the need to maximize the potential benefits to patients.

References:

Longo, D., & Drazen, J. (2016). Data Sharing New England Journal of Medicine, 374 (3), 276-277 DOI: 10.1056/NEJMe1516564
Wilbanks, J., & Friend, S. (2016). First, design for data sharing Nature Biotechnology DOI: 10.1038/nbt.3516
Bot, B., Suver, C., Neto, E., Kellen, M., Klein, A., Bare, C., Doerr, M., Pratap, A., Wilbanks, J., Dorsey, E., Friend, S., & Trister, A. (2016). The mPower study, Parkinson disease mobile data collected using ResearchKit Scientific Data, 3 DOI: 10.1038/sdata.2016.11

This article was published in the United Academics Magazine.

A perfectly frictionless world? No, thanks.

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“Pattern9” by F. Azuaje

“If all you have is a hammer, everything looks like a nail”

Abraham Maslow

Who can deny the advantages of a frictionless economy?

You need a ride to your next meeting, tap the app, boom! Here it comes.

You are in the mood for a chai latte, not too hot, not too spicy, and delivered to your home, push the bottom, and bam! You got it.

You saw that cute handbag, grab it, scan it, and wham! Off you go.

No bother, no red tape, no sweat.

Clearly, frictionless transactions are good for business. You sell more, I buy faster. You see it, you like it (or think you like it), just put it in the basket. And it is motivated by a smart insight: You do not want customers to think too much. The more time you spend buying it, the more likely that you won’t do it.

So who would not like a fully frictionless world?

The problem with this overarching desire is that the world is more than the worlds of retail and financial transactions. And our role in life does not have to be convenience maximization only.

But we can argue that a frictionless world will give us more time. Everything will be done for us in the “background” or somewhere in the cloud, and therefore we will have more opportunities for work and play. But if work and play are also frictionless, then all that is left to us is fruitless boredom and numbness.

The underlying concern here is that we are becoming accustomed to easily extrapolating the visions of the worlds of finance and commerce into the rest of the world: present and future.

Take, for example, a Scientific American article about the power of a frictionless society. According to it, frictionless apps will save democracy. The reasoning goes like this: the hassle of registering to vote and the process of actually voting creates friction, and friction is a variable in “the formula” for predicting someone’s probability to vote. Uhh!? Anyway, because of it, reducing friction would undoubtedly enhance turnout, which consequently would create “real democracies”.

Seriously? This is like suggesting that selling more cars will eradicate world hunger just because having more cars will increase the chances of bringing food to the needy.

And it is not difficult to find elsewhere other imaginative proposals for solving the world’s problems with frictionless technologies. Fighting malnutrition and the obesity epidemic? Tap the app, you get more kale and carrots. Curing diseases? Tap it, here are the data and somebody else will eventually discover new treatments. Making governments and companies more accountable? Tap it, here is Cremona, your personal chatbot.

Real-world problems seen through the single lens of high-tech and commerce not only downplays the complexity and importance of those problems. Also, by doing so, we are allowing someone, or something, to shift responsibility and costs onto others. The new president is an idiot, boom! You just tapped the wrong app. Infrastructure is crumbling, bam! We need a new app for that. You are not happy with the service, the result or the product? Sorry, we only offer the platform to enhance your experience. Hey, but now you have extra time to buy it again.

A perfectly frictionless world may not need us after all. We do not need people tapping around to get things done. Tapping is free, easy. Wait a second. Maybe we will still need a few people to develop the code for more tapping. But it is frictionless, remember? Who needs them either?

The aspiration of achieving a frictionless world is thus becoming an infantile idealism. Human interaction is reduced to screen scrolling. Learning becomes an effort to bypass any effort. Fun becomes thumb exercising. Notions of progress, wealth and health are increasingly distorted, and their realization are made almost impossible by the same people who thought that we could achieve that and more.

A little more friction here and there will not harm us. On the contrary, selected friction can enhance us as individuals, communities and economies. Just like in the domain of physical objects, friction can also create something new, polished and beautiful. Friction can also lead up to novelty in our daily routines, unique experiences, and more importantly: meaningful interactions with humans and other systems.

We should preserve the freedom to select the places and moments for some level of friction. A certain kind of friction that can make discovery and intelligent choice possible.

Published in: Medium.

Fairness In Science: What For?

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“Pattern8” by F. Azuaje

Journals and funding agencies ask their reviewers to be fair in their decisions. Committees are expected to act fairly in their evaluations concerning recruitment and promotions. Universities and other research organizations seek to embed fairness into their core values and policies.

And yet, what is it all for?

As a general rule, we understand fairness as the sense that the procedures behind any decision or interpretation potentially affecting us are: unbiased, proportionate and transparently applied.

This sounds like the central tenets of scientific research. Doesn’t it? Researchers usually know that they thrive and science advances only in the presence of these three attributes. But there is something stronger beneath that: science firmly rests on honesty, which in turns feeds on trust, and you cannot have trust where fairness is missing or ignored.

The scientific enterprise is mainly driven by the ambition of its members to achieve progress and to eventually benefit society: ranging from the most fundamental to the most applied aspects of their work. An almost blazing desire to search and to discover, the aspiration of advancing collectively or even of “moving up” as individuals. In science, like in free societies, such ambitions are typically constrained, or to a large extent moderated, by a strong set of norms and moral values. Norms and values that also rely on notions of fairness.

 

  “Give no decision till both sides thou’st heard”.

Phoclydes

Fairness in the knowledge economy

In an age of never-ending growth, performance-oriented organizations and technological “disruption”, fairness also has a more practical value.

Research shows that employees are more engaged when they feel that decisions are made and executed in a fair manner. There even seems to be a correlation between employees’ perceptions of a lack of fairness and straightforward negative behaviour, such as absenteeism and procedural noncompliance. But it is not just about boosting employee motivation and job satisfaction. It is more than that. It is also about creating the conditions for sustaining trustworthy, long-term collaborations. This is necessary if we wish to create environments where both ethical and productive behaviour can truly flourish.

So, fairness should go well beyond legalistic guidelines or self-congratulatory advertising of management excellence. It is both a fundamental value in science and a functional instrument for generating socio-economic benefits. Fairness helps us connect to our co-workers, employers and society at large. Conversely, unfairness can nurture an atmosphere of internally-destructive fear. Ah, fear: that measurable impediment to innovation .

And fear can also badly tear us apart: In science, as in democracies.

Sources:

Holtz, B., & Harold, C. (2010). Interpersonal Justice and Deviance: The Moderating Effects of Interpersonal Justice Values and Justice Orientation Journal of Management, 39 (2), 339-365 DOI: 10.1177/0149206310390049

Williams D.K. and Scott M.M. Conquering The Enemies of Innovation: Silence and Fear. Harvard Business Review 2012

Tricoles, R. (2012) Fairness in Workplace Key to Employee, Organizational Health

Originally published in: United Academics Magazine

Where Have All The Bad Scientific Talks Gone?

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“Pattern10” by F. Azuaje

Probably you have noticed that there is no room for bad talks at scientific meetings anymore. Where did all the average, or even the plain good, talks go? You only ever hear of “great” presentations.

The talk ends, here comes the first question: “thanks for the excellent presentation…” The second question: “thanks for the great presentation…”

Three quarters of the audience were bored to death, but it was an excellent talk. Half of the participants had no beeping idea of what that grey-suited guy was talking about, but what an amazing presentation.

Are all scientific presentations that good, sorry I meant: that excellent?

Seriously, there must be a mean level of quality. After all, eminent scientists can be crappy speakers, and eloquent speakers are not always a reflection of their contributions to a research field. Therefore, we should start hearing fewer “thanks for the excellent presentation” nuisance and more of old-fashion questioning and answering.  Scientific meetings are becoming the ground for banal exchanges of mutual validation.

Too much pop, a little more rock please

Hey, but as much as we enjoy an excellent presentation, let’s not forget all those first-rate questions from the audience.

“That’s a good question!” Says the academic prophet and even the mortal student at the podium. Suddenly, I hear so many good questions that I am also starting to wonder: “Where did all the bad questions go? What about the pointless questions? The “I know it too” questions? The questions to make sure that your colleagues or the speaker notice your attendance? What about some Marx Brothers or Cantinflas style of questions?” I miss those types of questions.

But wait a second; they are still with us. I hear them all the time! Or maybe this is just scientists being sarcastic. “Thanks for the excellent presentation, I was bored out of my skull, here is my question”. Or, from the side of the speaker: “ah that is a good question, and could you please stop behaving as if you were clueless?”

I want a little more no-nonsense attitude, and even wittiness, in scientific conferences. A little less self-marketing and crowd-pleasing statements please. Too many Taylor Swifts and Ed Sheerans in the crowd and at the stand of scientific research. We need many more Joe Strummers, Noel Gallaghers, Patti Smiths and Johnny Rottens of science.

OK, maybe you are right, just a few Johnny Rottens in science will suffice.

Or perhaps this is a well-intentioned effort to be kind and polite to each other. Do not get me wrong. Being nice is good. I mean excellent. But, nah, during coffee breaks I have heard plenty of frank opinions about presentations (and their accompanying questions!).

Excellent is the new average

We are in the age of excellence, in which almost everything is ground-breaking. Excellent is the new average.

These are times when sounding like you are right is more important that doing the right thing. But doing and saying the right thing means expressing something in an honest way. The problem is that intellectual honesty sometimes hurts: other people’s egos and possibly your own career prospects.

These are the days of fantastic. These seem to be times when having an opinion automatically makes you an expert, a visionary.

Everything is disruption without even making a splash.

Old are the days of average, heart-felt disagreement and intellectual discomfort. Maybe gone are the days when average was common, good meant good, and excellent was the tail of the statistical distribution. Excellent was not the average.

Edward O. Wilson, one of the world’s most admired scientists, advised young researchers that “the greatest proportion of moral decisions you will be required to make is in your relationships with other scientists”1.  And indeed this is a vital challenge, not only because science is above all a social networking endeavour, but also because an awareness of this reality may regrettably lead us to over-emphasize the importance of looking or sounding good to others.

And perhaps it is such an anxiety to find a cosy place in the nest of consensus among “peers” that is creating so much delusion.

We need to re-discover average, good and could-be-better. We can do it sincerely, kindly and with rational purpose. Only this way we will be able to spot the truly great.

1 Edward O. Wilson (2013). Letters to a young scientist Choice Reviews Online, 51 (02), 51-51 DOI: 10.5860/CHOICE.51-0846

Originally published in: United Academics Magazine.

Are Metaphors Just Handy Tools In Science?

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“Pattern18A” by F.Azuaje

Metaphors are probably a storyteller’s second best friend. The work of scientists and science journalists alike is populated by metaphors. Metaphors come in handy, and frequently go hand in hand with the answer to the scientific question investigated.

Thus, scientists investigate cells that fight infections, molecules that have mechanisms and biological processes that talk to each other. Chemical substances represent the building blocks of molecular routes and pathways. Tumours are capable to network with other tumours and microenvironments. Bacteria seem to enjoy a social life. Mathematical formulas have parameters that are learned from data. Genes are transcribed. Wormholes connect points in space-time. There are algorithms that are trained to complete tasks and computer chips that are expected to behave like brains. Genomes are blueprints and codes, and even contain fingerprints.

All beautifully began with a big bang. And all will eventually end with a big crunch.

The folly of mistaking a metaphor for a proof

“Metaphors are dangerous and are not to be trifled with” because “a single metaphor can give birth to love”, the writer Milan Kundera alerts us. Even in science, they are not less important. Not less seductive, not less dangerous.

Metaphors are not to be messed with. They can downplay our ignorance, amplify delusions and undervalue the gravity of our dilemmas. A gratuitous metaphor can excessively dumb things down and worse: make us look dumber. Poorly selected or overused metaphors can also be insulting, condescending and plain ridiculous. And let’s not forget the folly of mistaking a metaphor for a proof, as French poet Paul Valéry put it.

On another level, the American biologist Stephen Jay Gould thought that metaphors represented the deep barriers to scientific progress. This is because the latter greatly depends on having access to the right metaphor, not only to the requisite information, and because “revolutionary thinkers are not, primarily, gatherers of facts, but weavers of new intellectual structures.”. Or to put it another way, ground-breaking scientific progress requires more than new data, it relies on new visions.

Bringing clarity to the opaque

Metaphors in science can also be fascinating, fun and intriguing. They send us to streets we did not know were safe or apt for walking. They can connect worlds apart, or things we did not know could be put together. Metaphors remind us of the uniqueness of objects and ideas, and in parallel, of their unexpected interdependence.

Mostly, their significance resides in their potential for bringing clarity to the opaque, a little more light to the necessarily complex. For the journalist or scientist communicator, a metaphor is a common trick to not scare the reader. It helps us relieve the anguish of wanting to know something without having to know a lot. For the scientist, a metaphor can also become the actual thing that is described or sought. The glue needed to synthesise what is commonly difficult to break apart. They are used as pictures to allow us to see clearer, more expressive, pictures

But in the end, for better or worse, metaphors can simply become well-established habits. A highly convertible currency: in and across communities. Something that is useful, or at least convenient enough, to keep around in the family. That almost weird, sometimes embarrassing, but always handy uncle sitting in the corner.

Originally published in: United Academic Magazine